Over the years, technologies such as X-rays, Magnetic Resonance Imaging (MRIs) and other imaging technologies have been assisting doctors to assess and treat many types of health problems. However, for neck and back pain, it is often not possible to detect or correctly diagnose back pain simply by looking at patient X-rays. Often, patients report severe symptoms, but no apparent problems are found in the X-ray. Spine researchers have therefore explored more sophisticated methods to accurately diagnose spine related pain. Using the Mean Axis of Rotation (MAR) Analysis, which involves analyzing the cervical spine X-rays of a patient in extension and flexion positions, Mayer et al [1], linked patient symptoms to abnormal Mean Axis of Rotation (MAR) placement. Other studies also showed that MAR is a reliable measure for spine pathology in neck pain. However, Amevo et al [2] noted that different observers found the same mean location for the instantaneous axis at each segment in a test population of 17 normal subjects, but for any given subject the inter-observer and intra-observer differences were large, with a relative variation of up to 35%. Thus, while reliable for determining the mean location of the instantaneous axis of rotation in a population, the technique was not reliable for determining the axis in a given subject.
Amevo et al [3] modified the protocol. The reliability of a modified protocol for plotting the instantaneous axes of sagittal rotation for the cervical spine was evaluated by measuring the observer differences when the process was performed separately by two observers, and by a single observer, on two separate occasions. Small observer differences were found both for individual steps in the process and for the process as a whole. These differences were substantially less than those found using the conventional technique for plotting the instantaneous axes of rotation. The improvement in the technique was resultant from the use of stricter criteria for recognizing and tracing vertebral landmarks.
Amevo et al [4] then determined the locations of the instantaneous axes of rotation for the cervical motion segments C2 3 to C6 from flexion extension radiographs of 40 normal subjects using a modified overlay technique. The biological variation of the instantaneous axes of rotation was small, as was the technical error associated with the technique used. The data obtained enabled the formal definition of the normal range of locations for the instantaneous axes of rotation of the typical cervical motion segments.
In 2012, Desmoulin et al [5] used the MAR Analysis to demonstrate the success of Khan Kinetic Treatment (KKT), a non-invasive spine treatment for treating neck pain and back pain. It has been shown to do this by correcting the vertebral axis of rotation (alignment of the spine), as well as upregulating the genes within the discs, to give long term spinal health.
MAR is determined by analyzing the trajectory of cervical vertebrae as the patient moves from an extension position to a flexion position. To analyze the trajectory of vertebrae, each vertebra is traced in both positions, as shown in FIG. 1. For each pair of adjacent vertebrae, the upper vertebra moves along a circular trajectory relative to the lower vertebra, as shown in FIG. 1. The trajectory can be considered as a rotation about a specific axis of rotation. The axis of rotation can be calculated geometrically by first marking the translation vectors of 4 random points around the upper vertebra, from the extension position to the flexion position, with the lower vertebra fixed in space.
By bisecting the 4 translation vectors, the point of intersection of the bisecting lines represents the axis of rotation of the upper vertebra, as shown in FIG. 2. The location of the axis of rotation for a specific vertebra lies somewhere within or close to the vertebra below it. By placing a coordinate system about the bottom left corner of the lower vertebra, an (x,y) coordinate point can be assigned to the axis of rotation point, as shown in FIG. 3. This (x,y) coordinate point represents the MAR for the pair of adjacent vertebrae.
The steps to determine MAR manually are as follows:
Step 1 Trace: Manually trace C7 C2 vertebra on extension and flexion radiographs on acetate paper;
Step 2 Trace quality control: Compare each vertebra from the extension X-ray to the corresponding vertebrae in the flexion X-ray by superimposing the traces manually. The trace errors are manually identified and the average of the two traces is calculated and used for the remaining procedure;
Step 3 Calculate the MAR: Perform geometrical analysis to determine the movement of C2 C6 from Extension to Flexion, with respect to the adjacent lower vertebra, as described above (maximally superimpose C6 flexion trace with extension trace, mark four reference points on the four corners of the vertebra, overlap C7 of the flexion trace with C7 of the extension trace and mark new location of C6 reference point, then repeat for C2 to C5. Determine the MAR as described above); and
Step 4 Normalization: In order to use the MAR for comparative diagnosis, it is important to define the location of the MAR of a vertebrae pair by its position relative to the lower vertebra. To do this, a coordinate system is first placed with its origin around the lower vertebra, after which the location of the MAR can be expressed as a coordinate within this system. For example, in the case of the MAR for C6 C7, the coordinate system would be placed around the C7 vertebra. To allow comparison of MAR values across patients with different size vertebra, it is also important to represent the location of the MAR relative to the total size of the vertebra. To do this, the x coordinate of the MAR is normalized against the width of the vertebra, where the x coordinate will equal 1 if the x value of the MAR falls exactly on the right border of the vertebra. Similarly, the y coordinate of the MAR is normalized against the height of the vertebra, where the y coordinate will equal 1 if the y value of the MAR falls exactly on the top border of the vertebra. The MAR Normalization step will be required to normalize the MAR for each pair of vertebrae in the cervical spine, from C2 C3 pair to the C6 C7 pair.
Note that the exact placement of the coordinate system, as well as the placement of the top and left boundaries of the vertebra, requires qualitative judgment by the user, as the shapes of the vertebrae vary. Detailed instructions on how to place the coordinate system are provided in [5]. This step is performed manually. The final MAR coordinate can be classified according to the definition in [4].
While MAR Analysis promises to help the medical community diagnose patient conditions much more accurately than before, a major obstacle in its wide clinical use lies in the substantial effort (2 to 3 hours) required to perform the analysis accurately. Initially, the MAR procedure was completely manual, performed using tracing paper and manual geometric calculations. It was then semi-automated. The user would click on points around the border of each vertebra to provide a collection of points. The processor would then connect the points with straight lines between the points. For each vertebra in each X-ray, the collection of points and the lines therebetween defined a trace. In general, about 30 to about 60 clicks were used to define a trace. Higher resolution images required proportionally higher number of clicks. For a 2560*3072 pixel image, it required, on average, 200 clicks to adequately trace a single vertebra. If the user clicked on fewer points, the accuracy of the trace was compromised. This approach is tedious, time consuming and subject to significant error. Further, the data were collected and stored as integers, thus the data were inherently inaccurate and lacking precision. In order to programme for tracing by clicking, the code goes into a “Wait for Event” state. When the user clicked on the image (Mouse Click Event), an event was triggered, resulting in a point on the screen. The code then returned to the “Wait for Event” state and waited for the next event. Hence, the user needed to “trigger an event” for each point by clicking the mouse on the image.
As discussed, to calculate the MAR for a pair of vertebrae, the movement of the upper vertebra from extension to flexion is measured. This measurement is achieved by marking 4 points around the vertebra, and determining the displacement of each point from extension to flexion. The remaining MAR calculations are based on the displacement of these 4 points. In the Semi-Automated MAR Analysis tool, the 4 corners of the box bounding the collection of pixels representing the moving vertebra were used as the 4 points required for the MAR calculation. However, as these were points on an image, they were represented as a pair of integers, one for the X location, and one for the Y location of the point. The precision of an integer is 1, as there are no decimals. So, by calculating the MAR based on graphical points on the image, all of the data points were being rounded to the nearest 1, causing a loss of precision in the calculation.
Also, the MAR tool calculated an average trace by using NCC Pattern Matching to overlay the extension trace on the flexion trace. However, the best match found by the NCC Pattern Matching algorithm may not actually have been the correct match in reality. Recall that NCC is working only with binary images, whereas the real image is the actual X-Ray. It is possible that 2 different matches had the same “best match” score in the NCC algorithm, whereas only one of the matches worked on the actual image.
There have been a number of attempts to automate reading of medical images. For example, in U.S. Pat. No. 8,050,473 an improved method of segmenting medical images includes aspects of live wire and active shape models to determine the most likely segmentation given a shape distribution that satisfies boundary location constrains on an item of interest is provided. The method includes a supervised learning portion to train and learn new types of shape instances and a segmentation portion to use the learned model to segment new target images containing instances of the shape. The segmentation portion includes an automated search for an appropriate shape and deformation of the shape to establish a best oriented boundary for the object of interest on a medical image.
In U.S. Pat. No. 8,081,811 a method, apparatus, and program for judging image recognition results, and computer readable medium having the program stored therein is provided to obtain more accurate image recognition results while alleviating the burden on the user to check the image recognition results. An image recognition unit recognizes a predetermined structure in an image representing a subject, then a recognition result judging unit measures the predetermined structure on the image recognized by the image recognition unit to obtain a predetermined anatomical measurement value of the predetermined structure, automatically judges whether or not the anatomical measurement value falls within a predetermined standard range, and, if it is outside of the range, judges the image recognition result to be incorrect.
The objective of the present technology is to use computer vision techniques to substantially reduce the effort required to calculate the MAR, while maintaining the scientific and clinical accuracy of the procedure. It would be of further advantage if a user could intervene in the analysis due to the difficulty in matching traces of each vertebrae on the Extensions and Flexion views. It would be of further advantage if there was an auto-adjust feature that would compensate for inter-user differences in analysis, especially in light of the capability of user intervention. It would be of advantage if the trace could be smooth without having to click a large number of dots. It would be advantageous if the data could be collected as double precision numbers (15-17 decimal places of precision). It would be of further advantage if the collected data could be stored as double precision numbers.